The present invention relates generally to the field of linked data graphs, and more particularly to node differentiation in very large linked data graphs.
Linked data graphs cover a very broad domain, comprising knowledge from various fields, such as life sciences, geography and other areas of general interest. Linked Data Graphs have explicit, global semantics, such as the RDF. In other words, nodes and edges have a globally-valid meaning. This allows machine logic to integrate information across disparate systems. The richness of information available through integration comes at a price: the resulting graphs can be very large, creating a challenge when it comes to efficiently analyzing such structures. Currently, linked data contain over 40 billion edges, and the growing rate is considerable.
Risk analytics are techniques to identify and assess factors that influence achieving a goal. Machine logic implementing risk analytics is typically created based on expert knowledge.
Dijkstra's algorithm is a graph search algorithm that solves the single-source shortest path problem for a graph with non-negative edge path costs, producing a shortest path tree. Dijkstra's algorithm is often used in routing and as a subroutine in other graph algorithms. For a given source vertex (node) in the graph, the algorithm finds the path with lowest cost (that is, the shortest path) between that vertex and every other vertex. It can also be used for finding costs of shortest paths from a single vertex to a single destination vertex. This is done by stopping the algorithm once the shortest path to the destination vertex has been determined. For example, assume the vertices of the graph represent towns and edge path costs represent travel distances between pairs of towns connected by a direct road, Dijkstra's algorithm can be used to find the shortest route between one town and all other town. As a result, the shortest path algorithm is widely used in network routing protocols (for example, IS-IS and OSPF (Open Shortest Path First)).